title: Computerized model of cost-utility analysis for treatment of age-related macular degeneration.
abstract: OBJECTIVE
  To present a computerized model assessing individualized cost utility for current treatments for neovascular age-related macular degeneration (AMD) to enhance discussion regarding treatment options.
  
  
  METHODS
  Case- and eye-specific cost-utility analysis using individual case scenarios.
  
  
  METHODS
  Visual acuity data from published randomized controlled trials are incorporated into this analysis.
  
  
  METHODS
  Computerized model (Microsoft Visual Basic 6.0 programming) to establish preference-based cost-utility analysis in association with individual cost of treatment and blindness for neovascular AMD for both the better and worst seeing eye, with extrapolation of results over a 5-year term.
  
  
  METHODS
  Cost per quality-adjusted life-year (QALY) and cost per QALY gained for comparison of treatments for specific visual acuities.
  
  
  RESULTS
  All treatments show an increase in utility in comparison with best supportive care (BSC) if the better-seeing eye is treated. Ranibizumab, using the Phase IIIb, Multicenter, Randomized, Double-Masked, Sham Injection-Controlled Study of the Efficacy and Safety of Ranibizumab in Subjects with Subfoveal Choroidal Neovascularisation (CNV) with or without Classic CNV Secondary to AMD (PIER) regimen, is the most cost effective at $626 938 per QALY gained for treatment of the better seeing eye. To increase utility value when treating the worst seeing eye, the vision must improve to such a degree that it becomes the better seeing eye. This level of improvement is only possible if there is <9 letters difference between the 2 eyes and treated with ranibizumab. Over 5 years, increasing influence from the cost of blindness results in increasing costs for those treatments unable to stabilize vision. Within 5 years, the cost per QALY for the BSC is greater than all treatments except monthly ranibizumab injections.
  
  
  CONCLUSIONS
  Assessment of cost of treatment incorporates both effectiveness of treatment, cost of treatment, and cost of blindness. Cost analysis enables incorporation of these aspects of treatment with the quality of life data to provide a better comparison of treatments over time. This analysis has provided a method for individual analysis and therefore can provide the structure for resource allocation.
  
  
  BACKGROUND
  The authors have no proprietary or commercial interest in any materials discussed in this article.
authors.affiliation: King's College Hospital, London, United Kingdom. emily_cfletcher@yahoo.co.uk
authors.forename: E C
authors.initials: EC
authors.lastname: Fletcher
authors.forename: R J
authors.initials: RJ
authors.lastname: Lade
authors.forename: T
authors.initials: T
authors.lastname: Adewoyin
authors.forename: N V
authors.initials: NV
authors.lastname: Chong
mesh_terms: D020533:Angiogenesis Inhibitors / Q000191:economics*; D020256:Choroidal Neovascularization / Q000191:economics* / Q000209:etiology / Q000503:physiopathology; D003198:Computer Simulation; D017281:Cost of Illness; D003362:Cost-Benefit Analysis; D003663:Decision Trees; D006801:Humans; D008268:Macular Degeneration / Q000150:complications / Q000191:economics* / Q000503:physiopathology; D018803:Models, Economic; D010778:Photochemotherapy / Q000191:economics*; D011788:Quality of Life; D019057:Quality-Adjusted Life Years; D016032:Randomized Controlled Trials as Topic; D014792:Visual Acuity
chemical_list: D020533:Angiogenesis Inhibitors